ArticleDiagnostics (Basel, Switzerland)2021
Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- The global research of artificial intelligence on Alzheimer disease: A 25-year bibliometric analysis.Medicine · 2026Article
- Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.BMC musculoskeletal disorders · 2026Article
- Claustrum and Hippocampus Segmentation-Based Alzheimer's Disease Identification Model Using RoT-Kmeans and CoLU-CNN.Molecular neurobiology · 2026Article
- AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Rice pest detection via multi-scale edge network and wavelet attention enhancement.Frontiers in plant science · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Using artificial intelligence and computer vision to detect and monitor unhealthy child-directed food and beverage marketing: a data-driven study.EClinicalMedicine · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.Scientific reports · 2025Article
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints.Communications physics · 2025Article
- Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- A modified deep learning method for Alzheimer's disease detection based on the facial submicroscopic features in mice.Biomedical engineering online · 2024Article
- Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks.Journal of imaging · 2024Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.